{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn')\n",
    "sns.set(font_scale=2)\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：windows下可直接读取 \n",
    "\n",
    "linux下需要用软件(Editor等)将数据保存为utf-8格式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_PATH = './data/train_dataset/'\n",
    "TEST_PATH = './data/test_dataset/'\n",
    "\n",
    "df_train = pd.read_csv(TRAIN_PATH+'nCoV_100k_train.labled.csv',engine ='python')\n",
    "df_test = pd.read_csv(TEST_PATH+'nCov_10k_test.csv',engine ='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>微博id</th>\n",
       "      <th>微博发布时间</th>\n",
       "      <th>发布人账号</th>\n",
       "      <th>微博中文内容</th>\n",
       "      <th>微博图片</th>\n",
       "      <th>微博视频</th>\n",
       "      <th>情感倾向</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4456072029125500</td>\n",
       "      <td>01月01日 23:50</td>\n",
       "      <td>存曦1988</td>\n",
       "      <td>写在年末冬初孩子流感的第五天，我们仍然没有忘记热情拥抱这2020年的第一天。带着一丝迷信，早...</td>\n",
       "      <td>['https://ww2.sinaimg.cn/orj360/005VnA1zly1gah...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4456074167480980</td>\n",
       "      <td>01月01日 23:58</td>\n",
       "      <td>LunaKrys</td>\n",
       "      <td>开年大模型…累到以为自己发烧了腰疼膝盖疼腿疼胳膊疼脖子疼#Luna的Krystallife#?</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4456054253264520</td>\n",
       "      <td>01月01日 22:39</td>\n",
       "      <td>小王爷学辩论o_O</td>\n",
       "      <td>�偳癯空饩褪俏业�，爹，发烧快好，毕竟美好的假期拿来养病不太好，假期还是要好好享受快乐，爹，...</td>\n",
       "      <td>['https://ww2.sinaimg.cn/thumb150/006ymYXKgy1g...</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4456061509126470</td>\n",
       "      <td>01月01日 23:08</td>\n",
       "      <td>芩鎟</td>\n",
       "      <td>新年的第一天感冒又发烧的也太衰了但是我要想着明天一定会好的?</td>\n",
       "      <td>['https://ww2.sinaimg.cn/orj360/005FL9LZgy1gah...</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4455979322528190</td>\n",
       "      <td>01月01日 17:42</td>\n",
       "      <td>changlwj</td>\n",
       "      <td>问：我们意念里有坏的想法了，天神就会给记下来，那如果有好的想法也会被记下来吗？答：那当然了。...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               微博id        微博发布时间      发布人账号  \\\n",
       "0  4456072029125500  01月01日 23:50     存曦1988   \n",
       "1  4456074167480980  01月01日 23:58   LunaKrys   \n",
       "2  4456054253264520  01月01日 22:39  小王爷学辩论o_O   \n",
       "3  4456061509126470  01月01日 23:08         芩鎟   \n",
       "4  4455979322528190  01月01日 17:42   changlwj   \n",
       "\n",
       "                                              微博中文内容  \\\n",
       "0  写在年末冬初孩子流感的第五天，我们仍然没有忘记热情拥抱这2020年的第一天。带着一丝迷信，早...   \n",
       "1    开年大模型…累到以为自己发烧了腰疼膝盖疼腿疼胳膊疼脖子疼#Luna的Krystallife#?   \n",
       "2  �偳癯空饩褪俏业�，爹，发烧快好，毕竟美好的假期拿来养病不太好，假期还是要好好享受快乐，爹，...   \n",
       "3                     新年的第一天感冒又发烧的也太衰了但是我要想着明天一定会好的?   \n",
       "4  问：我们意念里有坏的想法了，天神就会给记下来，那如果有好的想法也会被记下来吗？答：那当然了。...   \n",
       "\n",
       "                                                微博图片 微博视频 情感倾向  \n",
       "0  ['https://ww2.sinaimg.cn/orj360/005VnA1zly1gah...   []    0  \n",
       "1                                                 []   []   -1  \n",
       "2  ['https://ww2.sinaimg.cn/thumb150/006ymYXKgy1g...   []    1  \n",
       "3  ['https://ww2.sinaimg.cn/orj360/005FL9LZgy1gah...   []    1  \n",
       "4                                                 []   []    1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 7 columns):\n",
      "微博id      100000 non-null int64\n",
      "微博发布时间    100000 non-null object\n",
      "发布人账号     100000 non-null object\n",
      "微博中文内容    99646 non-null object\n",
      "微博图片      100000 non-null object\n",
      "微博视频      100000 non-null object\n",
      "情感倾向      99919 non-null object\n",
      "dtypes: int64(1), object(6)\n",
      "memory usage: 75.0 MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     57619\n",
       "1     25392\n",
       "-1    16902\n",
       "·         1\n",
       "-2        1\n",
       "9         1\n",
       "-         1\n",
       "10        1\n",
       "4         1\n",
       "Name: 情感倾向, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['情感倾向'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗\n",
    "有脏数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train[df_train['情感倾向'].isin(['-1','0','1'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['情感倾向'] = df_train['情感倾向'].astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 99913 entries, 0 to 99999\n",
      "Data columns (total 7 columns):\n",
      "微博id      99913 non-null int64\n",
      "微博发布时间    99913 non-null object\n",
      "发布人账号     99913 non-null object\n",
      "微博中文内容    99560 non-null object\n",
      "微博图片      99913 non-null object\n",
      "微博视频      99913 non-null object\n",
      "情感倾向      99913 non-null int32\n",
      "dtypes: int32(1), int64(1), object(5)\n",
      "memory usage: 70.2 MB\n"
     ]
    }
   ],
   "source": [
    "# 发现有的部分还是有确实\n",
    "df_train.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>微博id</th>\n",
       "      <th>微博发布时间</th>\n",
       "      <th>发布人账号</th>\n",
       "      <th>微博中文内容</th>\n",
       "      <th>微博图片</th>\n",
       "      <th>微博视频</th>\n",
       "      <th>情感倾向</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4456072029125500</td>\n",
       "      <td>01月01日 23:50</td>\n",
       "      <td>存曦1988</td>\n",
       "      <td>写在年末冬初孩子流感的第五天，我们仍然没有忘记热情拥抱这2020年的第一天。带着一丝迷信，早...</td>\n",
       "      <td>['https://ww2.sinaimg.cn/orj360/005VnA1zly1gah...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4456074167480980</td>\n",
       "      <td>01月01日 23:58</td>\n",
       "      <td>LunaKrys</td>\n",
       "      <td>开年大模型…累到以为自己发烧了腰疼膝盖疼腿疼胳膊疼脖子疼#Luna的Krystallife#?</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4456054253264520</td>\n",
       "      <td>01月01日 22:39</td>\n",
       "      <td>小王爷学辩论o_O</td>\n",
       "      <td>�偳癯空饩褪俏业�，爹，发烧快好，毕竟美好的假期拿来养病不太好，假期还是要好好享受快乐，爹，...</td>\n",
       "      <td>['https://ww2.sinaimg.cn/thumb150/006ymYXKgy1g...</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4456061509126470</td>\n",
       "      <td>01月01日 23:08</td>\n",
       "      <td>芩鎟</td>\n",
       "      <td>新年的第一天感冒又发烧的也太衰了但是我要想着明天一定会好的?</td>\n",
       "      <td>['https://ww2.sinaimg.cn/orj360/005FL9LZgy1gah...</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4455979322528190</td>\n",
       "      <td>01月01日 17:42</td>\n",
       "      <td>changlwj</td>\n",
       "      <td>问：我们意念里有坏的想法了，天神就会给记下来，那如果有好的想法也会被记下来吗？答：那当然了。...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               微博id        微博发布时间      发布人账号  \\\n",
       "0  4456072029125500  01月01日 23:50     存曦1988   \n",
       "1  4456074167480980  01月01日 23:58   LunaKrys   \n",
       "2  4456054253264520  01月01日 22:39  小王爷学辩论o_O   \n",
       "3  4456061509126470  01月01日 23:08         芩鎟   \n",
       "4  4455979322528190  01月01日 17:42   changlwj   \n",
       "\n",
       "                                              微博中文内容  \\\n",
       "0  写在年末冬初孩子流感的第五天，我们仍然没有忘记热情拥抱这2020年的第一天。带着一丝迷信，早...   \n",
       "1    开年大模型…累到以为自己发烧了腰疼膝盖疼腿疼胳膊疼脖子疼#Luna的Krystallife#?   \n",
       "2  �偳癯空饩褪俏业�，爹，发烧快好，毕竟美好的假期拿来养病不太好，假期还是要好好享受快乐，爹，...   \n",
       "3                     新年的第一天感冒又发烧的也太衰了但是我要想着明天一定会好的?   \n",
       "4  问：我们意念里有坏的想法了，天神就会给记下来，那如果有好的想法也会被记下来吗？答：那当然了。...   \n",
       "\n",
       "                                                微博图片 微博视频  情感倾向  \n",
       "0  ['https://ww2.sinaimg.cn/orj360/005VnA1zly1gah...   []     0  \n",
       "1                                                 []   []    -1  \n",
       "2  ['https://ww2.sinaimg.cn/thumb150/006ymYXKgy1g...   []     1  \n",
       "3  ['https://ww2.sinaimg.cn/orj360/005FL9LZgy1gah...   []     1  \n",
       "4                                                 []   []     1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标签分布探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标签总体分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    0.576692\n",
       " 1    0.254141\n",
       "-1    0.169167\n",
       "Name: 情感倾向, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['情感倾向'].value_counts()/df_train['情感倾向'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1efe578bcc0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df_train['情感倾向'].value_counts()/df_train['情感倾向'].count()).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间对于标签分布的影响:  结论: 没有什么影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['time'] = pd.to_datetime('2020年' + df_train['微博发布时间'], format='%Y年%m月%d日 %H:%M', errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['date'] = df_train['time'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_influence = df_train.groupby(['date','情感倾向'],as_index=False).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 527.2x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.relplot(x=\"date\", y=\"微博id\", kind=\"line\", hue='情感倾向',palette=[\"b\", \"r\",'g'],data=date_influence)\n",
    "plt.xticks(rotation=90,fontsize=12)\n",
    "plt.xlabel('日期',fontsize=15)\n",
    "plt.ylabel('数量',fontsize=15)\n",
    "plt.title('微博数量分布图',fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_influence = date_influence.merge(df_train.groupby('date',as_index=False)['情感倾向'].count().rename(columns={'情感倾向':'weibo_count'}),how='left',on='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_influence['weibo_rate'] = date_influence['微博id']/date_influence['weibo_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 527.2x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1.22 中性的情感下降\n",
    "sns.relplot(x=\"date\", y=\"weibo_rate\", kind=\"line\", hue='情感倾向',palette=[\"b\", \"r\",'g'],data=date_influence)\n",
    "plt.xticks(rotation=90,fontsize=12)\n",
    "plt.xlabel('日期',fontsize=15)\n",
    "plt.ylabel('数量',fontsize=15)\n",
    "plt.title('微博情感占比分布图',fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 微博文本数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 微博文本长度分析\n",
    "- 基于字的长度\n",
    "- 基于词的长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['char_length'] = df_train['微博中文内容'].astype(str).apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(df_train['char_length'].tolist(),75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(df_train['char_length'].tolist(),99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['我', '爱', '北京', '天安门']\n"
     ]
    }
   ],
   "source": [
    "#北大开源的分词包\n",
    "#如果安装包的时候网速较慢的话可以如下安装：\n",
    "#pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pkuseg\n",
    "import pkuseg\n",
    "seg = pkuseg.pkuseg() \n",
    "text = seg.cut('我爱北京天安门')              # 进行分词\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['word_length'] = df_train['微博中文内容'].astype(str).apply(lambda x: len(seg.cut(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(df_train['word_length'].tolist(),99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 微博图片长度分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['pic_len'] = df_train['微博图片'].apply(lambda x: len(eval(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    48888\n",
       "0    32080\n",
       "2     4577\n",
       "3     4344\n",
       "9     4171\n",
       "4     2487\n",
       "6     2001\n",
       "5      795\n",
       "7      300\n",
       "8      270\n",
       "Name: pic_len, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['pic_len'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pic_len(target)')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['pic_len'].value_counts().plot.bar()\n",
    "plt.title('pic_len(target)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='pic_len', hue='情感倾向',data=df_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 微博视频长度分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['video_len'] = df_train['微博视频'].apply(lambda x: len(eval(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    77496\n",
       "1    22417\n",
       "Name: video_len, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['video_len'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'video_len(target)')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['video_len'].value_counts().plot.bar()\n",
    "plt.title('video_len(target)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结\n",
    "影响微博情感的可能有：\n",
    "- 微博文本内容\n",
    "- 微博图片内容\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}